{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b1a43a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\liangcheng\\AppData\\Local\\Temp\\ipykernel_1880\\1050079143.py:32: FutureWarning: 'w' is deprecated and will be removed in a future version, please use 'W' instead.\n",
      "  df = df.resample(freq).agg({'open': 'first', 'close': 'last'})\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "涨跌幅大于 2% 概率：0.3431\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator, MaxNLocator\n",
    "import datetime\n",
    "\n",
    "# === 用户可配置参数 ===\n",
    "freq = 'w'  # 数据频率：'D' 每日, 'W' 每周, 'M' 每月, 'Q' 每季度\n",
    "bin_width = 0.2  # X轴的涨跌幅刻度单位（百分比）\n",
    "years = 8  # 回溯年数\n",
    "\n",
    "# === 日期处理 ===\n",
    "today = datetime.datetime.today()\n",
    "full_start_date = (today - pd.DateOffset(years=years)).strftime('%Y%m%d')\n",
    "end_date = today.strftime('%Y%m%d')\n",
    "start_date = pd.to_datetime(full_start_date)\n",
    "\n",
    "# === 获取数据，注意此处 df 的列名应为英文 ===\n",
    "# df = 你实际获取的 DataFrame，列名应该已经是英文：date, open, close 等\n",
    "\n",
    "df = ak.stock_zh_a_daily(symbol=\"sz002594\", start_date=full_start_date, end_date=end_date, adjust=\"qfq\")\n",
    "df['date'] = pd.to_datetime(df['date'])  # 用英文字段\n",
    "df = df[['date', 'open', 'close']]  # 保留必要字段\n",
    "\n",
    "# 筛选数据\n",
    "df = df[df['date'] >= start_date].copy()\n",
    "df.sort_values('date', inplace=True)\n",
    "df.set_index('date', inplace=True)\n",
    "\n",
    "# 重采样（按频率转换）\n",
    "if freq != 'D':\n",
    "    df = df.resample(freq).agg({'open': 'first', 'close': 'last'})\n",
    "    df.dropna(inplace=True)\n",
    "\n",
    "# 添加涨跌幅（百分比）\n",
    "df['涨跌幅'] = df['close'].pct_change() * 100\n",
    "df.dropna(subset=['涨跌幅'], inplace=True)\n",
    "\n",
    "# === 绘图 ===\n",
    "fig, ax = plt.subplots(figsize=(18, 6))\n",
    "\n",
    "# 自动计算直方图区间数\n",
    "min_change = df['涨跌幅'].min()\n",
    "max_change = df['涨跌幅'].max()\n",
    "bins = int((max_change - min_change) / bin_width)\n",
    "n, bins, patches = ax.hist(df['涨跌幅'], bins=bins, rwidth=0.9, edgecolor='black')\n",
    "\n",
    "upper_bound = 2\n",
    "\n",
    "# 筛选在区间内的数据\n",
    "within_range = df[(df['涨跌幅'] >= upper_bound)]\n",
    "\n",
    "# 计算次数和概率\n",
    "count_within_range = len(within_range)\n",
    "total_count = len(df)\n",
    "probability_within_range = count_within_range / total_count\n",
    "\n",
    "# 打印结果\n",
    "print(f\"涨跌幅大于 {upper_bound}% 概率：{probability_within_range:.4f}\")\n",
    "\n",
    "# 设置X轴刻度间距\n",
    "ax.xaxis.set_major_locator(MultipleLocator(bin_width))\n",
    "\n",
    "# 设置颜色：涨（红），跌（绿）\n",
    "for patch in patches:\n",
    "    if patch.get_x() + patch.get_width() / 2 >= 0:\n",
    "        patch.set_facecolor('red')\n",
    "    else:\n",
    "        patch.set_facecolor('green')\n",
    "\n",
    "# 设置标签和标题\n",
    "ax.set_xlabel(\"涨跌幅（%）\", fontsize=12)\n",
    "ax.set_ylabel(\"出现次数\", fontsize=12)\n",
    "ax.set_title(f\"比亚迪（002594）近{years}年 {freq} 级别涨跌幅分布\", fontsize=14)\n",
    "\n",
    "# 设置Y轴为整数\n",
    "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "# X轴标签旋转\n",
    "plt.xticks(rotation=-90)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
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